GIS-based modeling of land use systems - Common Agricultural Policy reform and its impact on agricultural land use and plant species richness

An assessment of agricultural policy measures and their sustainability needs to consider economic, social, and ecological aspects. The current paradigm shift of the European Union’s Common Agricultural Policy (CAP) from coupled to decoupled transfer payments calls for such an evaluation. Land users have to reevaluate their production program and its spatial allocation. Consequently, agricultural policy influences regional land use patterns and shares of land use systems, which in turn influence regional plant species richness. Connecting land use and ecological models allows to assess socioeconomic and ecologic effects of policy measures by identifying interactions and estimating potential trade-offs. The paper presents the land use model ProLand and the fuzzy expert system UPAL. ProLand models the regional distribution of land use systems while UPAL predicts plant species richness. The models are connected through a GIS and applied to a study area in Hesse, Germany, in order to simulate the effects of changing conditions on land use, economic and social key indicators, and plant species richness. ProLand is a spatially explicit comparative static model that simulates a region’s land use pattern based on natural, socioeconomic, political, and technological parameters. The model assumes land rent maximizing behavior of land users. It calculates and assigns the land rent maximizing land use system for every investigated decision unit, generally a field. A land use system is characterized through crop rotation, corresponding outdoor operations, animal husbandry if applicable, and the relevant political and socioeconomic attributes. The fuzzy expert system derives the values of ecologically relevant parameters from several site specific attributes and land use operations. Land use dependent site characteristics that influence plant species richness are derived from predictions generated by ProLand. Detailed information on crop rotation, fertilization and pesticide strategy, and outdoor operations are considered. The expert system then classifies natural and land use dependent site characteristics into aggregate factors. Based on a set of rules it assigns the number of species to the classes and thus to the decision units. Simulation results for the study area show that the CAP reform causes a rise in grassland area. These land use changes mainly occur in areas currently used for arable farming but with natural conditions favoring grassland. Plant species richness is positively influenced by the increase in extensive grassland area.

Optimal greenhouse cultivation control: survey and perspectives

Abstract: A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well

Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation

[EN]Monitoring mechanisms that ensure efficient crop growth are essential on many farms, especially in certain areas of the planet where water is scarce. Most farmers must assume the high cost of the required equipment in order to be able to streamline natural resources on their farms. Considering that many farmers cannot afford to install this equipment, it is necessary to look for more effective solutions that would be cheaper to implement. The objective of this study is to build virtual organizations of agents that can communicate between each other while monitoring crops. A low cost sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining the amount of resources the crops need at every moment. Since the hardware has limited processing and communication capabilities, our approach uses the PANGEA architecture to overcome this limitation. Specifically, we will design a system that is capable of collecting heterogeneous information from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from sensors and produce a response adapted to the context. In order to validate the proposed system, we present a case study in which farmers are provided with a tool that allows us to monitor the condition of crops on a TV screen using a low cost device.European Commision (EC). Funding H2020/MSCARISE. Project Code: 641794European Commision (EC). Funding FP7/SPE/SME. Project Code: 283638European Commision (EC). Funding FP7/SP1/ENV. Project Code: 28294